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Although regression models play a central role in the analysis of medical research projects, there still exist many misconceptions on various aspects of modeling leading to faulty analyses. Indeed, the rapidly developing statistical methodology and its recent advances in regression modeling do not seem to be adequately reflected in many medical publications. This problem of knowledge transfer from statistical research to application was identified by some medical journals, which have published series of statistical tutorials and (shorter) papers mainly addressing medical researchers. The aim of this review was to assess the current level of knowledge with regard to regression modeling contained in such statistical papers. We searched for target series by a request to international statistical experts. We identified 23 series including 57 topic-relevant articles. Within each article, two independent raters analyzed the content by investigating 44 predefined aspects on regression modeling. We assessed to what extent the aspects were explained and if examples, software advices, and recommendations for or against specific methods were given. Most series (21/23) included at least one article on multivariable regression. Logistic regression was the most frequently described regression type (19/23), followed by linear regression (18/23), Cox regression and survival models (12/23) and Poisson regression (3/23). Most general aspects on regression modeling, e.g. model assumptions, reporting and interpretation of regression results, were covered. We did not find many misconceptions or misleading recommendations, but we identified relevant gaps, in particular with respect to addressing nonlinear effects of continuous predictors, model specification and variable selection. Specific recommendations on software were rarely given. Statistical guidance should be developed for nonlinear effects, model specification and variable selection to better support medical researchers who perform or interpret regression analyses.

Before organizing mixed-mode data collection for the self-administered questionnaire of the Belgian Health Interview Survey, measurement effects between the paper-and-pencil and the web-based questionnaire were evaluated. A two-period cross-over study was organized with a sample of 149 employees of two Belgian research institutes (age range 22-62 years, 72% female). Measurement agreement was assessed for a diverse range of health indicators related to general health, mental and psychosocial health, health behaviors and prevention with kappa coefficients and intraclass correlation (ICC). The quality of the data collected by both modes was evaluated by quantifying the missing, 'don't know' and inconsistent values and data entry mistakes. Good to very good agreement was found for all categorical indicators with kappa coefficients superior to 0.60, except for two mental and psychosocial health indicators namely the presence of a sleeping disorder and of a depressive disorder (kappa≥0.50). For the continuous indicators high to acceptable agreement was observed with ICC superior to 0.70. Inconsistent answers and data-entry mistakes were only occurring in the paper-and-pencil mode. There were no less missing values in the web-based mode compared to the paper-and-pencil mode. The study supports the idea that web-based modes provide, in general, equal responses to paper-and-pencil modes. However, health indicators based upon factual and objective items tend to have higher measurement agreement than indicators requiring an assessment of personal subjective feelings. A web-based mode greatly facilitates the data-entry process and guides the completing of a questionnaire. However, item non-response was not positively affected. ispartof: PLOS ONE vol:13 issue:5 ispartof: location:United States status: published

Objective To survey the knowledge, attitude, and practices of Vietnamese pharmacists regarding the COVID-19 pandemic. Method This cross-sectional, paper-based study was conducted from June to August 2020. A validated questionnaire (Cronbach’s alpha = 0.84) was used to interview 1,023 pharmacists in nine provinces of Vietnam. Analysis of covariance was employed to identify factors associated with the knowledge of pharmacists. The best model was chosen by using the Bayesian Model Averaging method in R software version 4.0.4. Results The mean knowledge score was 12.02 ± 1.64 (range: 6–15), which indicated that 93.4% of pharmacists had good knowledge of COVID-19. There was no difference in the average score between males and females (p > 0.05). The multivariate linear regression model revealed that the knowledge was significantly associated with pharmacists’ age, education level, and residence (p < 0.001). About attitude and practices, pharmacists daily sought and updated information on the COVID-19 pandemic through mass media and the internet (social network and online newspapers). Nearly 48% of them conceded that they communicated with customers when at least one person did not wear a face mask at the time of the COVID-19 outbreak. At medicine outlets, many measures were applied to protect pharmacists and customers, such as equipping pharmacists with face masks and hand sanitizers (95.0%), using glass shields (83.0%), and maintaining at least one-meter distance between two people (85.2%). Conclusion The pharmacists’ knowledge of COVID-19 transmission, symptoms, and prevention was good. Many useful measures against the spread of this perilous virus were applied in medicine outlets. However, pharmacists should restrict forgetting to wear face masks in communication with medicine purchasers. The government and health agencies should have practical remedies to reduce the significant differences in the COVID-19 knowledge of pharmacists among provinces and education-level groups.

We propose to use Twitter data as social-spatial sensors. This study deals with the question whether research papers on certain diseases are perceived by people in regions (worldwide) that are especially concerned by these diseases. Since (some) Twitter data contain location information, it is possible to spatially map the activity of Twitter users referring to certain papers (e.g., dealing with tuberculosis). The resulting maps reveal whether heavy activity on Twitter is correlated with large numbers of people having certain diseases. In this study, we focus on tuberculosis, human immunodeficiency virus (HIV), and malaria, since the World Health Organization ranks these diseases as the top three causes of death worldwide by a single infectious agent. The results of the social-spatial Twitter maps (and additionally performed regression models) reveal the usefulness of the proposed sensor approach. One receives an impression of how research papers on the diseases have been perceived by people in regions that are especially concerned by these diseases. Our study demonstrates a promising approach for using Twitter data for research evaluation purposes beyond simple counting of tweets.

Background Adiponectin is an adipocyte-derived hormone that acts as a marker of insulin sensitivity. Bloodspot sampling by fingerstick onto filter paper may increase the feasibility of large-scale studies of the determinants of insulin sensitivity. We first describe the validation of an enzyme-linked immunoassay (ELISA) for quantifying adiponectin from dried blood spots and then demonstrate its application in a large trial (PROBIT). Methods We quantified adiponectin from 3-mm diameter discs (≈3 µL of blood) punched from dried blood spots obtained from: i) whole blood standards (validation); and ii) PROBIT trial samples (application) in which paediatricians collected blood spots from 13,879 children aged 11.5 years from 31 sites across Belarus. We examined the distribution of bloodspot adiponectin by demographic and anthropometric factors, fasting insulin and glucose. Results In the validation study, mean intra-assay coefficients of variation (n = 162) were 15%, 13% and 10% for ‘low’ (6.78 µg/ml), ‘medium’ (18.18 µg/ml) and 'high’ (33.13 µg/ml) internal quality control (IQC) samples, respectively; the respective inter-assay values (n = 40) were 23%, 21% and 14%. The correlation coefficient between 50 paired whole bloodspot versus plasma samples, collected simultaneously, was 0.87 (95% CI: 0.78 to 0.93). Recovery of known quantities of adiponectin (between 4.5 to 36 µg/ml) was 100.3–133%. Bloodspot adiponectin was stable for at least 30 months at −80°C. In PROBIT, we successfully quantified fasting adiponectin from dried blood spots in 13,329 of 13,879 (96%) children. Mean adiponectin (standard deviation) concentrations were 17.34 µg/ml (7.54) in boys and 18.41 µg/ml (7.92) in girls and were inversely associated with body mass index, fat mass, triceps and subscapular skin-fold thickness, waist circumference, height and fasting glucose. Conclusions Bloodspot ELISA is suitable for measuring adiponectin in very small volumes of blood collected on filter paper and can be applied to large-scale studies.

Nicola Low and colleagues announce a call for research papers on sexually transmitted infections, to accompany a Collection on the topic.

Informed consent is the cornerstone of human research subject protection. Many subjects sign consent documents without understanding the study purpose, procedures, risks, benefits, and their rights. Proof of comprehension is not required and rarely obtained. Understanding might improve by using an interactive system with multiple options for hearing, viewing and reading about the study and the consent form at the subject’s own pace with testing and immediate feedback. This prospective randomized study compared the IRB-approved paper ICF for an actual clinical research study with an interactive presentation of the same study and its associated consent form using an iPad device in two populations: clinical research professionals, and patients drawn from a variety of outpatient practice settings. Of the 90 participants, 69 completed the online test and survey questions the day after the session (maximum 36 hours post-session). Among research professionals (n = 14), there was a trend (p = .07) in the direction of iPad subjects testing better on the online test (mean correct = 77%) compared with paper subjects (mean correct = 57%). Among patients (n = 55), iPad subjects had significantly higher test scores than standard paper consent subjects (mean correct = 75% vs 58%, p < .001). For all subjects, the total time spent reviewing the paper consent was 13.2 minutes, significantly less than the average of 22.7 minutes total on the three components to be reviewed using the iPad (introductory video, consent form, interactive quiz). Overall satisfaction and overall enjoyment slightly favored the interactive iPad presentation. This study demonstrates that combining an introductory video, standard consent language, and an interactive quiz on a tablet-based system improves comprehension of research study procedures and risks.

In recent years, the number of reported Human African Trypanosomiasis (HAT) cases caused by Trypanosoma brucei (T.b.) gambiense has been markedly declining, and the goal of ‘elimination as a public health problem’ is within reach. For the next stage, i.e. interruption of HAT transmission by 2030, intensive screening and surveillance will need to be maintained, but with tools and strategies more efficiently tailored to the very low prevalence. We assessed the sequential use of ELISA and Immune Trypanolysis (ITL) on dried blood spot (DBS) samples as an alternative to the traditional HAT field testing and confirmation approach. A cross-sectional study was conducted in HAT endemic and previously endemic zones in Kongo Central province, and a non-endemic zone in Haut Katanga province in the Democratic Republic of the Congo (DRC). Door-to-door visits were performed to collect dried blood spot (DBS) samples on filter paper. ELISA/T.b. gambiense was conducted followed by ITL for those testing positive by ELISA and in a subset of ELISA negatives. In total, 11,642 participants were enrolled. Of these, 11,535 DBS were collected and stored in appropriate condition for ELISA testing. Ninety-seven DBS samples tested positive on ELISA. In the endemic zone, ELISA positivity was 1.34% (95%CI: 1.04–1.64). In the previously endemic zone and non-endemic zone, ELISA positivity was 0.34% (95% CI: 0.13–0.55) and 0.37% (95% CI: 0.15–0.60) respectively. Among the ELISA positives, only two samples had a positive ITL result, both from the endemic zone. One of those was from a former HAT patient treated in 2008 and the other from an individual who unfortunately had deceased prior to the follow-up visit. Our study showed that a surveillance strategy, based on DBS samples and centralized testing with retracing of patients if needed, is feasible in DRC. ELISA seems well suited as initial test with a similar positivity rate as traditional screening tests, but ITL remains complex. Alternatives for the latter, also analyzable on DBS, should be further explored., Author summary Human African Trypanosomiasis (HAT), also known as sleeping sickness is a parasitic disease, transmitted by tsetse flies, that is usually fatal if untreated. The number of cases have been rapidly declining over the past years indicating that elimination of the disease as a public health problem is within reach. To achieve the next stage, i.e. interruption of HAT transmission by 2030, intensive screening and surveillance will need to be maintained, but with tools and strategies more efficiently tailored to the very low prevalence. In contrast to the traditional approach of sending laboratory expertise to the field, we assessed an alternative approach based on the collection of dried blood samples on filter paper that were tested in a regional laboratory. Samples were taken in endemic, previously endemic and non-endemic villages and tested by ELISA and Immune Trypanolysis. The ELISA positivity rates were similar to those of other screening techniques currently used and Immune Trypanolysis was highly specific. Hence for surveillance in HAT endemic areas, collecting dried blood samples followed by centralized testing could become an alternative to the current strategy of active screening by mobile teams with on the spot confirmation. It has also potential for post-elimination surveillance to monitor resurgence and for exploratory surveillance in historic foci. Though highly specific, Immune Trypanolysis remains too complex for use in intermediate level laboratories, to further expand this strategy an alternative second step test is required.

Extensive research shows that residential segregation has severe health consequences for racial and ethnic minorities. Most research to date has operationalized segregation in terms of either poverty or race/ethnicity rather than a synergy of these factors. A novel version of the Index of Concentration at the Extremes (ICERace-Income) specifically assesses racialized economic segregation in terms of spatial concentrations of racial and economic privilege (e.g., wealthy white people) versus disadvantage (e.g., poor Black people) within a given area. This multidimensional measure advances a more comprehensive understanding of residential segregation and its consequences for racial and ethnic minorities. The aim of this paper is to critically review the evidence on the association between ICERace-Income and health outcomes. We implemented the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct a rigorous search of academic databases for papers linking ICERace-Income with health. Twenty articles were included in the review. Studies focused on the association of ICERace-Income with adverse birth outcomes, cancer, premature and all-cause mortality, and communicable diseases. Most of the evidence indicates a strong association between ICERace-Income and each health outcome, underscoring income as a key mechanism by which segregation produces health inequality along racial and ethnic lines. Two of the reviewed studies examined racial disparities in comorbidities and health care access as potential explanatory factors underlying this relationship. We discuss our findings in the context of the extant literature on segregation and health and propose new directions for future research and applications of the ICERace-Income measure.

Background & methods National protests in the summer of 2020 drew attention to the significant presence of police in marginalized communities. Recent social movements have called for substantial police reforms, including “defunding the police,” a phrase originating from a larger, historical abolition movement advocating that public investments be redirected away from the criminal justice system and into social services and health care. Although research has demonstrated the expansive role of police to respond a broad range of social problems and health emergencies, existing research has yet to fully explore the capacity for health insurance policy to influence rates of arrest in the population. To fill this gap, we examine the potential effect of Medicaid expansion under the Affordable Care Act (ACA) on arrests in 3,035 U.S. counties. We compare county-level arrests using FBI Uniform Crime Reporting (UCR) Program Data before and after Medicaid expansion in 2014–2016, relative to counties in non-expansion states. We use difference-in-differences (DID) models to estimate the change in arrests following Medicaid expansion for overall arrests, and violent, drug, and low-level arrests. Results Police arrests significantly declined following the expansion of Medicaid under the ACA. Medicaid expansion produced a 20–32% negative difference in overall arrests rates in the first three years. We observe the largest negative differences for drug arrests: we find a 25–41% negative difference in drug arrests in the three years following Medicaid expansion, compared to non-expansion counties. We observe a 19–29% negative difference in arrests for violence in the three years after Medicaid expansion, and a decrease in low-level arrests between 24–28% in expansion counties compared to non-expansion counties. Our main results for drug arrests are robust to multiple sensitivity analyses, including a state-level model. Conclusions Evidence in this paper suggests that expanded Medicaid insurance reduced police arrests, particularly drug-related arrests. Combined with research showing the harmful health consequences of chronic policing in disadvantaged communities, greater insurance coverage creates new avenues for individuals to seek care, receive treatment, and avoid criminalization. As police reform is high on the agenda at the local, state, and federal level, our paper supports the perspective that broad health policy reforms can meaningfully reduce contact with the criminal justice system under historic conditions of mass criminalization.

Background Ward-Based Outreach Teams’ (WBOTs) use of mobile technologies can promote better quality and improved health services for populations in low- and middle-income countries. However, the implementation of such systems is fraught with threats to sustainability. Purpose The purpose of this study was to gain a deeper understanding of users’ views and experiences of the implementation of mHealth in a selected sub-district in North West Province, South Africa. Methods The study was qualitative, exploratory and descriptive. Data were collected from community health workers (CHWs) (n = 24) and outreach team leaders (OTLs) (n = 4) who used mobile devices loaded with the Mobenzi application through semi-structured focus group interviews, using an interview guide. Creswell’s stages of thematic analysis were used for data analysis, and codes, categories and themes were validated with the participants. Results Three themes emerged from the findings, namely the transition from a paper-based system to an electronic system; the use of the application; and reverting to paper-based documentation. The findings revealed that WBOTs considered the mHealth application as useful, easy to use, and supportive to their workflow. They experienced some technical challenges and lamented the inaccessibility of technicians. The mobile initiative’s termination caused distress among the participants, especially CHWs who had to revert to paper-based documentation to capture community health data. OTLs were concerned about the quality of health data being captured in the absence of the application. Conclusions The study concluded that community-based health care programmes that use WBOTs and mHealth technologies are essential in strengthening the health care system. WBOTs are facilitators for improving access to health care. Policy-makers and district managers will gain useful insights if they engage application users in discussions regarding future mobile health interventions for household and community-based care.

Background During the Coronavirus Disease 2019 (COVID-19) pandemic, the number of consultations and diagnoses in primary care and referrals to specialist care declined substantially compared to prepandemic levels. Beyond deferral of elective non-COVID-19 care by healthcare providers, it is unclear to what extent healthcare avoidance by community-dwelling individuals contributed to this decline in routine healthcare utilisation. Moreover, it is uncertain which specific symptoms were left unheeded by patients and which determinants predispose to healthcare avoidance in the general population. In this cross-sectional study, we assessed prevalence of healthcare avoidance during the pandemic from a patient perspective, including symptoms that were left unheeded, as well as determinants of healthcare avoidance. Methods and findings On April 20, 2020, a paper COVID-19 survey addressing healthcare utilisation, socioeconomic factors, mental and physical health, medication use, and COVID-19–specific symptoms was sent out to 8,732 participants from the population-based Rotterdam Study (response rate 73%). All questionnaires were returned before July 10, 2020. By hand, prevalence of healthcare avoidance was subsequently verified through free text analysis of medical records of general practitioners. Odds ratios (ORs) for avoidance were determined using logistic regression models, adjusted for age, sex, and history of chronic diseases. We found that 1,142 of 5,656 included participants (20.2%) reported having avoided healthcare. Of those, 414 participants (36.3%) reported symptoms that potentially warranted urgent evaluation, including limb weakness (13.6%), palpitations (10.8%), and chest pain (10.2%). Determinants related to avoidance were older age (adjusted OR 1.14 [95% confidence interval (CI) 1.08 to 1.21]), female sex (1.58 [1.38 to 1.82]), low educational level (primary education versus higher vocational/university 1.21 [1.01 to 1.46), poor self-appreciated health (per level decrease 2.00 [1.80 to 2.22]), unemployment (versus employed 2.29 [1.54 to 3.39]), smoking (1.34 [1.08 to 1.65]), concern about contracting COVID-19 (per level increase 1.28 [1.19 to 1.38]) and symptoms of depression (per point increase 1.13 [1.11 to 1.14]) and anxiety (per point increase 1.16 [1.14 to 1.18]). Study limitations included uncertainty about (perceived) severity of the reported symptoms and potentially limited generalisability given the ethnically homogeneous study population. Conclusions In this population-based cross-sectional study, 1 in 5 individuals avoided healthcare during lockdown in the COVID-19 pandemic, often for potentially urgent symptoms. Healthcare avoidance was strongly associated with female sex, fragile self-appreciated health, and high levels of depression and anxiety. These results emphasise the need for targeted public education urging these vulnerable patients to timely seek medical care for their symptoms to mitigate major health consequences., Marije J. Splinter and colleagues assess the prevalence of healthcare avoidance during the COIVD-19 pandemic and investigate related determinants, Author summary Why was this study done? ➢ During the Coronavirus Disease 2019 (COVID-19) pandemic, consultation rates in both primary and specialist care declined compared to prepandemic levels, which can partially be attributed to the postponement or cancellation of elective and nonurgent medical care. ➢ It is unclear to what extent these declines in consultation rates could be related to healthcare avoidance by patients in the general population. ➢ To evaluate the collateral health damage of the COVID-19 pandemic, it is important to not only assess the prevalence of healthcare avoidance, but also for what symptoms healthcare was avoided and which determinants are associated with this behaviour. What did the researchers do and find? ➢ We sent out a paper questionnaire to 8,732 participants of the population-based Rotterdam Study containing several COVID-19–related subjects, such as healthcare utilisation, work status, mental and physical health, and concerns about contracting COVID-19. ➢ About 6,241 participants (73%) returned the questionnaire, of whom 5,656 participants (90.6%) were included in our analyses. We found that 1,142 of them (20.2%) avoided healthcare during the COVID-19 pandemic, often for symptoms that might have needed urgent medical evaluation, such as limb weakness (13.6%), palpitations (10.8%), and chest pain (10.2%). ➢ Determinants that were most strongly associated with healthcare avoidance were female sex, poor self-appreciated health, and high levels of depression and anxiety. What do these findings mean? ➢ The results of this population-based study suggest that healthcare avoidance contributed to the decline in consultation rates during the COVID-19 pandemic. Importantly, our findings suggest that this behaviour may be associated with certain vulnerable groups within the population. ➢ These findings should be interpreted in light of the limitations of this study, which include that the actual severity of the symptoms that were reported by participants is unknown, since they were not medically evaluated when they experienced these symptoms. ➢ The findings of this study can be used to develop policy interventions targeted to vulnerable individuals who may be more likely to exhibit healthcare avoiding behaviours.

In this paper we review the methodological underpinnings of the general pharmacogenetic approach for uncovering genetically-driven treatment effect heterogeneity. This typically utilises only individuals who are treated and relies on fairly strong baseline assumptions to estimate what we term the ‘genetically moderated treatment effect’ (GMTE). When these assumptions are seriously violated, we show that a robust but less efficient estimate of the GMTE that incorporates information on the population of untreated individuals can instead be used. In cases of partial violation, we clarify when Mendelian randomization and a modified confounder adjustment method can also yield consistent estimates for the GMTE. A decision framework is then described to decide when a particular estimation strategy is most appropriate and how specific estimators can be combined to further improve efficiency. Triangulation of evidence from different data sources, each with their inherent biases and limitations, is becoming a well established principle for strengthening causal analysis. We call our framework ‘Triangulation WIthin a STudy’ (TWIST)’ in order to emphasise that an analysis in this spirit is also possible within a single data set, using causal estimates that are approximately uncorrelated, but reliant on different sets of assumptions. We illustrate these approaches by re-analysing primary-care-linked UK Biobank data relating to CYP2C19 genetic variants, Clopidogrel use and stroke risk, and data relating to APOE genetic variants, statin use and Coronary Artery Disease., Author summary Understanding how much a specific treatment’s effect is moderated by common genetic variation is an important public health question. If a person’s genetics means they will experience a much reduced treatment effect, as measured with respect to a particular health outcome, then they could be switched to an alternative therapy. When assessing the impact of such a switch at the population level, it is typical to only use data on those who are treated with the said drug. However, this analysis is compromised if genetic variants exist which moderate the treatment effect and affect the outcome through alternative pathways. In this paper we describe an extended analysis framework to estimating the ‘genetically moderated treatment effect’ (GMTE) that incorporates information on both treated and untreated individuals. With this larger set of information we show that four analysis approaches for estimating the GMTE are possible. Each one relies on a different set of assumptions to work correctly and provides estimates that are largely uncorrelated with one another. Our paper describes a decision framework for triangulating the findings from these four approaches in order to provide a more robust basis for decision making in public health.

Mathematical models are powerful tools in HIV epidemiology, producing quantitative projections of key indicators such as HIV incidence and prevalence. In order to improve the accuracy of predictions, such models need to incorporate a number of behavioural and biological heterogeneities, especially those related to the sexual network within which HIV transmission occurs. An individual-based model, which explicitly models sexual partnerships, is thus often the most natural type of model to choose. In this paper we present PopART-IBM, a computationally efficient individual-based model capable of simulating 50 years of an HIV epidemic in a large, high-prevalence community in under a minute. We show how the model calibrates within a Bayesian inference framework to detailed age- and sex-stratified data from multiple sources on HIV prevalence, awareness of HIV status, ART status, and viral suppression for an HPTN 071 (PopART) study community in Zambia, and present future projections of HIV prevalence and incidence for this community in the absence of trial intervention., Author summary In this paper we present PopART-IBM, an individual-based model used to simulate HIV transmission in communities in high prevalence settings. We show that PopART-IBM can simulate transmission over a span of decades in a large community in less than a minute. This computational efficiency allows us to calibrate the model within an inference framework, and we show an illustrative example of calibration using an adaptive population Monte Carlo Approximate Bayesian Computation algorithm for a community in Zambia that was part of the HPTN-071 (PopART) trial. We compare the detailed model output to real-world data collected during the trial from this community. Finally, we project how the HIV epidemic would have changed over time in this community if no intervention from the trial had occurred.

Against the current COVID-19 pandemic, governments worldwide have devised a variety of non-pharmaceutical interventions to suppress it, but the efficacy of distinct measures is not yet well quantified. In this paper, we propose a novel tool to achieve this quantification. In fact, this paper develops a new extended epidemic SEIR model, informed by a socio-political classification of different interventions, to assess the value of several suppression approaches. First, we inquire the conceptual effect of suppression parameters on the infection curve. Then, we illustrate the potential of our model on data from a number of countries, to perform cross-country comparisons. This gives information on the best synergies of interventions to control epidemic outbreaks while minimising impact on socio-economic needs. For instance, our results suggest that, while rapid and strong lock-down is an effective pandemic suppression measure, a combination of social distancing and contact tracing can achieve similar suppression synergistically. This quantitative understanding will support the establishment of mid- and long-term interventions, to prepare containment strategies against further outbreaks. This paper also provides an online tool that allows researchers and decision makers to interactively simulate diverse scenarios with our model.

Surveillance is critical to mounting an appropriate and effective response to pandemics. However, aggregated case report data suffers from reporting delays and can lead to misleading inferences. Different from aggregated case report data, line list data is a table contains individual features such as dates of symptom onset and reporting for each reported case and a good source for modeling delays. Current methods for modeling reporting delays are not particularly appropriate for line list data, which typically has missing symptom onset dates that are non-ignorable for modeling reporting delays. In this paper, we develop a Bayesian approach that dynamically integrates imputation and estimation for line list data. Specifically, this Bayesian approach can accurately estimate the epidemic curve and instantaneous reproduction numbers, even with most symptom onset dates missing. The Bayesian approach is also robust to deviations from model assumptions, such as changes in the reporting delay distribution or incorrect specification of the maximum reporting delay. We apply the Bayesian approach to COVID-19 line list data in Massachusetts and find the reproduction number estimates correspond more closely to the control measures than the estimates based on the reported curve., Author summary Interventions meant to control infectious diseases are often determined and judged using surveillance data on the number of new cases of disease. In many diseases, there are substantial delays between the time when an individual is infected or shows symptoms and when the case is actually reported to a public health authority, such as the CDC. This reported data often collects information on symptom onset dates for some individuals. In this paper, we describe a method that imputes missing onset dates for all individuals and recreates the history of the disease progression in a population according to symptom onset dates, which are the best observable proxy available for infection dates. Our method also estimates the instantaneous reproduction number and is robust to many deviations from the assumptions of the model. We show, using a COVID-19 dataset from Massachusetts that our method accurately follows the implementation of control measures in the state.

This study establishes a new method for estimating the monthly Average Population Present (APP) in Australian regions. Conventional population statistics, which enumerate people where they usually live, ignore the significant spatial mobility driving short term shifts in population numbers. Estimates of the temporary or ambient population of a region have several important applications including the provision of goods and services, emergency preparedness and serve as more appropriate denominators for a range of social statistics. This paper develops a flexible modelling framework to generate APP estimates from an integrated suite of conventional and novel data sources. The resultant APP estimates reveal the considerable seasonality in small area populations across Australia’s regions alongside the contribution of domestic and international visitors as well as absent residents to the observed monthly variations. The modelling framework developed in the paper is conceived in a manner such that it can be adapted and re-deployed both for use with alternative data sources as well as other situational contexts for the estimation of temporary populations.

Taking the Guangdong-Hong Kong-Macao Greater Bay Area as the research area, this paper used OD cluster analysis based on Baidu migration data from January 11 to January 25 (before the sealing-off of Wuhan) and concluded that there is a significant correlation 1the migration level from Wuhan to the GBA and the epidemic severity index. This paper also analyzed the migration levels and diffusivity of the outer and inner cities of the GBA. Lastly, four evaluation indexes were selected to research the possibility of work resumption and the rating of epidemic prevention and control through kernel density estimation. According to the study, the amount of migration depends on the geographical proximity, relationship and economic development of the source region, and the severity of the epidemic depends mainly on the migration volume and the severity of the epidemic in the source region. The epidemic risk is related not only to the severity of the epidemic in the source region but also to the degree of urban traffic development and the degree of urban openness. After the resumption of work, the pressure of epidemic prevention and control has been concentrated mainly in Shenzhen and Canton; the further away a region is from the core cities, the lower the pressure in that region. The mass migration of the population makes it difficult to control the epidemic effectively. The study of the relationship between migration volume, epidemic severity and epidemic risk is helpful to further analyze transmission types and predict the trends of the epidemic.

This paper uses event study based on the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model to study the impact of the COVID-19 outbreak on China’s financial market. It finds that the pandemic had an overall significant and negative impact on the stock prices of firms listed on SSE, SZSE and ChiNext. However, such impact appeared to be heterogeneous across industries, affecting listed firms in industries such as pharmaceutical and telecommunications positively, but those in services industries such as accommodation, catering, and commercial services negatively. Apparently, a crisis for some had been an opportunity for others. In addition, this paper seeks to understand the micro mechanism behind the heterogeneity of pandemic shock from the perspective of firms’ financial position. It finds that listed firms with higher debt level were hit harder, whereas those with more net cash flow had displayed higher resilience against the blow of the pandemic. However, the opposite pattern is found among those listed on ChiNext and in industries severely devastated by the pandemic. These findings have policy implications in terms of preventing systemic financial risks and facilitating recovery during pandemic-induced economic downturns. It also helps investor adjust investment strategies, hedge against risks, and secure gains when the market conditions in general are unfavorable.

The skill of analyzing and interpreting research data is central to the scientific process, yet it is one of the hardest skills for students to master. While instructors can coach students through the analysis of data that they have either generated themselves or obtained from published articles, the burgeoning availability of preprint articles provides a new potential pedagogical tool. We developed a new method in which students use a cognitive apprenticeship model to uncover how experts analyzed a paper and compare the professional’s cognitive approach to their own. Specifically, students first critique research data themselves and then identify changes between the preprint and final versions of the paper that were likely the results of peer review. From this activity, students reported diverse insights into the processes of data presentation, peer review, and scientific publishing. Analysis of preprint articles is therefore a valuable new tool to strengthen students’ information literacy and understanding of the process of science.

COVID-19 leads small and medium-sized enterprises (SMEs) to survive very hard. The development difficulties of SMEs lead to weak employment and GDP growth in various countries. In the process of COVID-19’s continuous spread, what is the major reason for the difficulties of SMEs? This paper hopes to answer this question by studying SMEs in Beijing. On this basis, this paper uses structural equation model (SEM) to study the relatively fast recovery of SMEs in Beijing, China, to explore the factors affecting SMEs in the pandemic. After detailed desk research and interviews with relevant entrepreneurs, this paper collects 234 valid questionnaires from SMEs in various industries in Beijing with the help of Federation of Industry and Commerce and Chamber of Commerce in Beijing. Then the data is analyzed with the SEM, which shows the relationship between cash flow from financing activities, markets, employees, costs, government policies and the impact of the pandemic. Finally, an impact model of the pandemic on SMEs is established. The result of the model indicates that the direct effect of the pandemic on the market is the most prominent, and government policies can significantly reduce the negative impact of the pandemic on SMEs indirectly. Based on this, this paper puts forward some policy suggestions, such as the targeted issuance of consumption vouchers and the reduction of administrative barriers. This will enable megacities in various countries to improve policy support for SMEs and promote the recovery and development of SMEs.

Since the outbreak of the global COVID-19 pandemic, social distancing has been known to everyone and recommended almost everywhere everyday. Social distancing has been and will be one of the most effective measures and sometimes, the only available one for fighting epidemics and saving lives. However, it has not been so clear how social distancing should be practiced or managed, especially when it comes to regulating everyone’s otherwise normal social activities. The debate on how to implement social distancing often leads to a heated political argument, while research on the subject is lacking. This paper is to provide a theoretical basis for the understanding of the scientific nature of social distancing by considering it as a social dilemma game played by every individual against his/her population. From this perspective, every individual needs to make a decision on how to engage in social distancing, or risk being trapped into a dilemma either exposing to deadly diseases or getting no access to necessary social activities. As the players of the game, the individual’s decisions depend on the population’s actions and vice versa, and an optimal strategy can be found when the game reaches an equilibrium. The paper shows how an optimal strategy can be determined for a population with either closely related or completely separated social activities and with either single or multiple social groups, and how the collective behaviors of social distancing can be simulated by following every individual’s actions as the distancing game progresses. The simulation results for populations of varying sizes and complexities are presented, which not only justify the choices of the strategies based on the theoretical analysis, but also demonstrate the convergence of the individual actions to an optimal distancing strategy in silico and possibly in natura as well, if every individual makes rational distancing decisions.

Background A pandemic of coronavirus disease 2019 (COVID-19) emerged and affected most of the world in early 2020. To inform effective public health measures we conducted a knowledge, attitude and practice (KAP) survey among a Hausa Muslim society in Nigeria in March 2020. Methods The study is an analytic cross-sectional survey with questionnaires administered to the general population including Health Care Workers (HCW) in Kano, Nigeria. Participants were recruited by convenience sampling following informed consent. The percentage of KAP scores were categorized as good and poor. Independent predictors of good knowledge of COVID 19 were ascertained using a binary logistic regression model. Results The questionnaire was administered among urban 32.8%, peri-urban dwellers 32.4%, and to online participants 34.8%. The peri-urban and urban participants were given paper questionnaires. There were 886 study participants with mean age 28.58yrs [SD:10.25] (Interquartile range [IQR]:22yrs–32yrs), males 55.4% with 57.3% having had or were in tertiary education. Most participants were students 40% and civil servants 20%. The overall mean [standard deviation (SD)] for knowledge, attitude and practice scores expressed in percentage was 65.38%[SD15.90], 71.45% [SD14.10], and 65.04% [SD17.02] respectively. Out of the respondents, 270(30.47%) had good knowledge (GK), 158(17.8%) had good attitude (GA), and 230(25.96%) had good practice (GP) using cut-off scores of 75%, 86.5%, and 75% respectively. Over 48% did not agree COVID-19 originated from animals while 60% perceived the pandemic to be due to God’s punishment. Also, 36% thought it was a man-made virus. When rating fear, most respondents [63.5%] had marked fear i.e. ≥ 7 out of 10 and 56% admitted to modifying their habits recently in fear of contracting the virus. As regards attitude to religious norms, 77.77% agreed on cancellation of the lesser pilgrimage as a measure to curb the spread of the disease while 23.64% admitted that greater pilgrimage (Hajj) should proceed despite the persistence of the ongoing pandemic. About 50% of the respondents insisted on attending Friday congregational prayers despite social distancing. One in four people still harbored stigma towards a person who has recovered from the virus. 28% felt some races are more at risk of the disease though 66% mentioned always practicing social distancing from persons coughing or sneezing. Almost 70% of respondents said they were willing to accept a vaccine with 39% saying they would be willing to pay for it if not publicly funded. In univariate analysis increasing age and having been ever married were associated with GK while tertiary education was associated with GA [Odds Ratio; 95% Confidence Interval] 2.66(1.79–3.95). Independent positive predictors of GK were those who were or had ever been married, those who had marked fear of COVID-19, and had modified their habits in the last three months. Those who had non-tertiary education and had the questionnaire administered as paper rather than online version had GK but age was not a predictor. Conclusion Knowledge of transmission and preventive measures should be improved in the general population cognizant of cultural norms and Islamic practices. The study highlights the importance of considering belief systems and perception in developing control measures against COVID-19.

At present, people are demanding better indoor air quality during the COVID-19 pandemic. In addition to maintaining the basic functions, new air-conditioning should also add air purification functions to improve indoor air quality and reduce the possibility of virus transmission. Nowadays, there is lack of research results on the innovation of air-conditioning. The aim of this study is to present a two-stage mathematical model for identifying critical manufacturing factors in the innovation process of air conditioning. In this paper, Kano and quality function deployment (QFD) are used to analyze the critical factors affecting air-conditioning innovation. Some studies have proposed using Kano-QFD model to analyze product innovation, but the study only studies one stage, which loses the analysis of the subsequent stages of product innovation. Based on this, this paper studies the priority method of two-stage critical factors for air-conditioning innovation. Firstly, the questionnaire survey and fuzzy sets are used to collect demand information of multi-agent (customers and professional technicians). Secondly, the Kano model is used to classify and calculate satisfaction of multi-agent. Then, QFD is used to transform multi-agent demands into engineering property indexes (first stage) and technical property indexes (second stage) and calculate the weight of each index. Finally, the applicability and superiority of this method is illustrated by taking the central air-conditioning as an example.

Background and objectives With the increase in the number of COVID-19 infections, the global health apparatus is facing insufficient resources. The main objective of the current study is to provide additional data regarding the clinical characteristics of the patients diagnosed with COVID-19, and in particular to analyze the factors associated with disease severity, lack of improvement, and mortality. Methods 102 studies were included in the present meta-analysis, all of which were published before September 24, 2020. The studies were found by searching a number of databases, including Scopus, MEDLINE, Web of Science, and Embase. We performed a thorough search from early February until September 24. The selected papers were evaluated and analyzed using Stata software application version 14. Results Ultimately, 102 papers were selected for this meta- analysis, covering 121,437 infected patients. The mean age of the patients was 58.42 years. The results indicate a prevalence of 79.26% for fever (95% CI: 74.98–83.26; I2 = 97.35%), 60.70% for cough (95% CI: 56.91–64.43; I2 = 94.98%), 33.21% for fatigue or myalgia (95% CI: 28.86–37.70; I2 = 96.12%), 31.30% for dyspnea (95% CI: 26.14–36.69; I2 = 97.67%), and 10.65% for diarrhea (95% CI: 8.26–13.27; I2 = 94.20%). The prevalence for the most common comorbidities was 28.30% for hypertension (95% CI: 23.66–33.18; I2 = 99.58%), 14.29% for diabetes (95% CI: 11.88–16.87; I2 = 99.10%), 12.30% for cardiovascular diseases (95% CI: 9.59–15.27; I2 = 99.33%), and 5.19% for chronic kidney disease (95% CI: 3.95–6.58; I2 = 96.42%). Conclusions We evaluated the prevalence of some of the most important comorbidities in COVID-19 patients, indicating that some underlying disorders, including hypertension, diabetes, cardiovascular diseases, and chronic kidney disease, can be considered as risk factors for patients with COVID-19 infection. Furthermore, the results show that an elderly male with underlying diseases is more likely to have severe COVID-19.

Background Mycetoma, a chronic infection of the skin and underlying structures, affects those with a close relationship to the land, often in resource-poor areas of the world. Whether caused by any one of a variety of fungus or bacteria, mycetoma causes significant disability and mortality. Acknowledged as a neglected tropical disease (NTD) by the World Health Organization (WHO) in 2016, mycetoma is susceptible to being misunderstood, misdiagnosed, and mismanaged. In an effort to shift the balance in favor of recognition and effective treatment, sound epidemiological understanding is required. Methods and findings In this paper, a literature review of case reports and series (332 papers in total) is presented as three maps. We identified 19,494 cases dating from 1876 to 2019, with cases contracted in 102 countries. The first map shows where mycetoma has ever been reported, the second shows how many cases have been reported, and the third shows the ratio of eumycetoma (fungal) to actinomycetoma (bacterial). Most cases are found in Mexico, India, and Sudan, where mycetoma is studied rigorously. We identified emergence of new geographical loci, including the United States, Venezuela, Italy, China, and Australia. Notably, mycetoma is reported far outside the tropics. In the Americas, bacterial forms dominate, whereas, in Africa and Asia, the picture is more varied. Conclusions With better understanding of the epidemiology of mycetoma, more can be done to direct education, preventive measures, and treatment to at-risk areas, enabling a reduction in disease burden., Author summary Mycetoma is an ancient and misunderstood chronic disease. Affecting the skin and subcutaneous structures, infection often occurs in the feet of land laborers and can be caused by several otherwise rare human pathogens. Traditionally seen as a tropical disease, it affects those in resource-poor regions. The best methods of diagnosis are rarely available in these areas; late diagnosis increases the need for invasive surgical treatment. Available medical treatment can be prohibitively expensive. Early diagnosis and identification of the causative agent is necessary for effective treatment. For this paper, a literature review was undertaken to find all published cases of mycetoma linked to a location and the data used to create three maps. The maps show where mycetoma has ever been reported, how many cases have been reported, and what kind of microorganism was present. We included 19,494 cases, most of which occurred in Mexico, Sudan, and India. Notably, some cases arose far outside the tropics. In the Americas, most cases are bacterial; elsewhere, the picture is more varied. Many gaps remain in the epidemiological study of mycetoma, but this paper aims to shed light on the significant reach of the disease, with a view to increasing its visibility on the global stage and, therefore, the scope for reducing the burden of this neglected tropical disease.

Replication is an important tool to promote high quality research and ensure policy makers can rely on studies in making guidelines or funding programs. By ensuring influential studies are replicable we provide assurance that the policies based on these studies are well-founded and the conclusions and recommendations are robust-to different estimation models or different choices. In this paper, we argue that replication is not only useful but necessary to ensure that an author's choice in how to analyse data is not the only factor that determines whether an intervention is effective or not. We also show that while most research is done well and provides robust results, small differences can lead to different interpretations and these differences need to be acknowledged. This special issue highlights 5 such replication studies, which are replications of influential studies on biomedical, social, behavioural and structural interventions for HIV prevention and treatment. We reflect on their findings. Four out of five studies, which conduct push button replication and pure replication, were able to reproduce the results of the original studies with minor differences, mainly due to minor typographical errors or rounding differences. The analysis of the measurement and estimation analyses conducted in these five studies reveals that the original results are not very robust to alternative analytical approaches, especially when these results rely on a small number of observations. In these cases, the original results are weakened. Furthermore, in contrast to the original papers, two of the five included replication studies conducted a theory of change analysis-to explore how or why the interventions work (or do not) not just whether the intervention works or not. These two analyses indicate that the estimated impacts of the interventions are drawn from few mediators. In addition, they demonstrate that, in some cases, a lack of effect may be related to lack of adequate exposure to the intervention rather than inefficacy of the intervention per se. However, overall, the included replication studies show that the results presented in the original papers are trustworthy and robust, especially when based on larger sample sizes. Replication studies can not only verify the results of a study, they can also provide additional insights on the published results, such as how and why an intervention was effective or less effective than expected. They can thus be a tool to inform the research community and/ or policymakers about whether and how interventions could be adopted, which need to be tested further, and which should be discontinued because of their ineffectiveness. Thus, publishing these replication studies in peer-reviewed journals makes the work public and publicized. The work advances knowledge, and publication should be encouraged, as it is for other types of research.

Background As the world’s largest coal producer, China was accounted for about 46% of global coal production. Among present coal mining risks, methane gas (called gas in this paper) explosion or ignition in an underground mine remains ever-present. Although many techniques have been used, gas accidents associated with the complex elements of underground gassy mines need more robust monitoring or warning systems to identify risks. This paper aimed to determine which single method between the PCA and Entropy methods better establishes a responsive weighted indexing measurement to improve coal mining safety. Methods Qualitative and quantitative mixed research methodologies were adopted for this research, including analysis of two case studies, correlation analysis, and comparative analysis. The literature reviewed the most-used multi-criteria decision making (MCDM) methods, including subjective methods and objective methods. The advantages and disadvantages of each MCDM method were briefly discussed. One more round literature review was conducted to search publications between 2017 and 2019 in CNKI. Followed two case studies, correlation analysis and comparative analysis were then conducted. Research ethics was approved by the Shanxi Coking Coal Group Research Committee. Results The literature searched a total of 25,831publications and found that the PCA method was the predominant method adopted, and the Entropy method was the second most widely adopted method. Two weighting methods were compared using two case studies. For the comparative analysis of Case Study 1, the PCA method appeared to be more responsive than the Entropy. For Case Study 2, the Entropy method is more responsive than the PCA. As a result, both methods were adopted for different cases in the case study mine and finally deployed for user acceptance testing on 5 November 2020. Conclusions The findings and suggestions were provided as further scopes for further research. This research indicated that no single method could be adopted as the better option for establishing indexing measurement in all cases. The practical implication suggests that comparative analysis should always be conducted on each case and determine the appropriate weighting method to the relevant case. This research recommended that the PCA method was a dimension reduction technique that could be handy for identifying the critical variables or factors and effectively used in hazard, risk, and emergency assessment. The PCA method might also be well-applied for developing predicting and forecasting systems as it was sensitive to outliers. The Entropy method might be suitable for all the cases requiring the MCDM. There is also a need to conduct further research to probe the causal reasons why the PCA and Entropy methods were applied to each case and not the other way round. This research found that the Entropy method provides higher accuracy than the PCA method. This research also found that the Entropy method demonstrated to assess the weights of the higher dimension dataset was higher sensitivity than the lower dimensions. Finally, the comprehensive analysis indicates a need to explore a more responsive method for establishing a weighted indexing measurement for warning applications in hazard, risk, and emergency assessments.

This paper analyzes the dynamics of the labor market in Latin America during the COVID-19 pandemic. After a decade of a virtuous circle of growth with the creation of formal jobs, the pandemic has had an considerable impact on the region’s labor market, generating an unparalleled increase in the proportion of the inactive population, considerable reductions in informality, and, in contrast, smaller fluctuations in formal jobs. In this context, the formal sector, given its lower flexibility, became a "social safety net" that preserved the stability of employment and wages. Based on the findings presented in this paper, it is projected that, starting in 2021, informality will grow to levels higher than those of the pre-COVID-19 era–with 7.56 million additional informal jobs–as a result of the population returning to the labor market to compensate for the declines in incomes. According to the simulations presented, postponing or forgiving income tax payments and social security contributions conditional on the generation of formal jobs could reduce the growth of informality by 50 to 75 percent. Achieving educational improvements has the potential to reduce it by 50 percent.

An unbiased, widely accepted estimate of the rate of occurrence of new cases of autism over time would facilitate progress in understanding the causes of autism. The same may also apply to other disorders. While incidence is a widely used measure of occurrence, birth prevalence—the proportion of each birth year cohort with the disorder—is the appropriate measure for disorders and diseases of early childhood. Studies of autism epidemiology commonly speculate that estimates showing strong increases in rate of autism cases result from an increase in diagnosis rates rather than a true increase in cases. Unfortunately, current methods are not sufficient to provide a definitive resolution to this controversy. Prominent experts have written that it is virtually impossible to solve.This paper presents a novel method, time-to-event birth prevalence estimation (TTEPE), to provide accurate estimates of birth prevalence properly adjusted for changing diagnostic factors. It addresses the shortcomings of prior methods. TTEPE is based on well-known time-to-event (survival) analysis techniques. A discrete survival process models the rates of incident diagnoses by birth year and age. Diagnostic factors drive the probability of diagnosis as a function of the year of diagnosis. TTEPE models changes in diagnostic criteria, which can modify the effective birth prevalence when new criteria take effect. TTEPE incorporates the development of diagnosable symptoms with age. General-purpose optimization software estimates all parameters, forming a non-linear regression. The paper specifies all assumptions underlying the analysis and explores potential deviations from assumptions and optional additional analyses.A simulation study shows that TTEPE produces accurate parameter estimates, including trends in both birth prevalence and the probability of diagnosis in the presence of sampling effects from finite populations. TTEPE provides high power to resolve small differences in parameter values by utilizing all available data points.

The paper examines the interest of the commercial banks' stakeholders in Pillar 3 disclosures and their behaviour during the timing of serious market turbulence. The aim is to discover to which extent current banking regulation supports stakeholders' interest in the information required by regulators to be disclosed. The examined data consists of log files that were pre-processed using web mining techniques and from which were extracted frequent item sets by quarters and evaluated in terms of quantity. The authors have proposed a methodology to evaluate frequent item sets of web parts over a dedicated time. Based on the verification of applied methodology on two commercial banks, the results show that stakeholders' interest in disclosures is highest in the first quarter at each year and after turbulent times in 2009 their interests decreased. Moreover, the results suggest that stakeholders expressed higher interest than in regulatory required Pillar 3 information in the following group of information: Pillar3 related information, Annual reports, Information on Group. Following our results, the paper contributes to cover the gap in the research by analysing Pillar 3 disclosures and their compliance with regulatory requirements, which also increase the interest of the relevant stakeholders to conduce them as an effective market discipline tool. Článek zkoumá zájem zainteresovaných stran komerčních bank o zveřejňování informací podle 3. pilíře a jejich chování v době vážných tržních turbulencí. Cílem je zjistit, do jaké míry současná bankovní regulace podporuje zájem zainteresovaných stran o informace, jejichž zveřejnění regulátoři vyžadují. Zkoumaná data se skládají z logů, které byly předem zpracovány pomocí technik web miningu a z nichž byly extrahovány soubory častých položek podle čtvrtletí a vyhodnoceny z hlediska kvantity. Autoři navrhli metodiku vyhodnocování sad častých položek webových částí za vyhrazený čas. Na základě ověření aplikované metodiky na dvou komerčních bankách výsledky ukazují, že zájem zainteresovaných stran o zveřejňované informace je v každém roce nejvyšší v prvním čtvrtletí a po turbulentním období v roce 2009 jejich zájem poklesl. Výsledky dále naznačují, že zainteresované strany projevily vyšší zájem než o regulatorně požadované informace 3. pilíře o následující skupinu informací: Informace související s pilířem 3, Výroční zprávy, Informace o skupině. V návaznosti na naše výsledky přispívá tento článek k pokrytí mezery ve výzkumu analýzou informací zveřejňovaných v rámci Pilíře 3 a jejich souladu s regulatorními požadavky, což také zvyšuje zájem příslušných zainteresovaných stran o jejich vedení jako účinného nástroje tržní disciplíny.

Selecting a target journal is a universal decision faced by authors of scientific papers. Components of the decision, including expected turnaround time, journal acceptance rate, and journal impact factor, vary in terms of accessibility. In this study, I collated recent turnaround times and impact factors for 82 journals that publish papers in the field of fisheries sciences. In addition, I gathered acceptance rates for the same journals when possible. Findings indicated clear among-journal differences in turnaround time, with median times-to-publication ranging from 79 to 323 days. There was no clear correlation between turnaround time and acceptance rate nor between turnaround time and impact factor; however, acceptance rate and impact factor were negatively correlated. I found no field-wide differences in turnaround time since the beginning of the COVID-19 pandemic, though some individual journals took significantly longer or significantly shorter to publish during the pandemic. Depending on their priorities, authors choosing a target journal should use the results of this study as guidance toward a more informed decision.

Drowning is a global public health problem, but accurately estimating drowning risk remains a challenge. Coastal drowning comprises a significant proportion of the drowning burden in Australia and is influenced by a range of behavioural factors (e.g. risk perception, knowledge, attitudes and behaviours) that are poorly understood. These factors, along with those that impact exposure (e.g. coastal visitation and activity participation) all impact on drowning risk. While excellent mortality and morbidity data exists in Australia, a lack of coastal participation data presents challenges to identifying high-risk groups or activities and prioritising prevention efforts. This methods paper describes the development and evolution of an ongoing, annual, nationally representative online survey as an effective tool used to capture valuable data about the Australian population’s relationship with the coast. This paper explores how the survey is structured (12–14 sections spanning multiple topics and themes), the different question types used (including open text, 4-digit responses and categorical questions), the sample size (1400–1600 respondents), sampling strategy (using demographic quota sampling which can then be post-weighted to the population if required) and how topics and themes have changed over time to enhance the quality of data collected (i.e., wording changes to enhance participant comprehension or data usability and changing issue-specific ‘feature’ topics of interest such as campaign evaluation). How the survey is implemented online is described, both practically through to third-party recruitment processes and ethically to maximise anonymity of respondents and ensure data quality. Interim analyses indicate the impact of considering exposure when calculating fatal drowning rates, especially by activity (e.g., crude boating drowning rate 0.12 per 100,000 population vs 0.95 per 100,000 exposed population [relative risk = 8.01; 95% confidence interval: 4.55–14.10]). This study highlights lessons learned in the process of conducting a nationally representative coastal participation survey as well as the strengths and limitations of adopting this approach. Data collected will provide more detailed information on the skills, behaviours, knowledge and attitudes of coastal activity participants. Analyses of this unique dataset will inform research that will underpin development and evaluation of coastal drowning prevention initiatives prioritising those most at risk. It is hoped that the methods detailed within this study may be useful for other countries to develop similar approaches to understanding their own population.

In this paper, we propose a hierarchical statistical model for a single repairable system subject to several failure modes (competing risks). The paper describes how complex engineered systems may be modelled hierarchically by use of Bayesian methods. It is also assumed that repairs are minimal and each failure mode has a power-law intensity. Our proposed model generalizes another one already presented in the literature and continues the study initiated by us in another published paper. Some properties of the new model are discussed. We conduct statistical inference under an objective Bayesian framework. A simulation study is carried out to investigate the efficiency of the proposed methods. Finally, our methodology is illustrated by two practical situations currently addressed in a project under development arising from a partnership between Petrobras and six research institutes.

Since the financial system has illustrated an increasingly prominent characteristic of inextricable connections, information theory is gradually utilized to study the financial system. By collecting the daily data of industry index (2005-2020) and region index (2012-2020) listed in China as samples, this paper applies an innovative measure named partial mutual information on mixed embedding to generate directed networks. Based on the analysis of nonlinear relationships among sectors, this paper realizes the accurate construction of “time-varying” financial network from the perspective of risk spillover. The results are presented as follow: (1) interactions can be better understood through the nonlinear networks among distinct sectors, and sectors in the networks could be classified into different types according to their topological properties connected to risk spillover; (2) in the rising stage, information is transmitted rapidly in the network, so the risk is fast diffused and absorbed; (3) in the declining stage, the network topology is more complex and panic sentiments have long term impact leading to more connections; (4) The US market, Japan market and Hongkong market have significant affect on China’s market. The results suggest that this nonlinear measure is an effective approach to develop financial networks and explore the mechanism of risk spillover.

The SARS-CoV-2 pandemic has emerged as one of the most dramatic health crises of recent decades. This paper treats mainstream news about the current pandemic as a valuable entry point for analyzing the relationship between science and politics in the public sphere, where the outbreak must be both understood and confronted through appropriate public-health policy decisions. In doing so, the paper aims to examine which actors, institutions, and experts dominate the SARS-CoV-2 media narratives, with particular attention to the roles of political, medical, and scientific actors and institutions within the pandemic crisis. The study relies on a large dataset consisting of all SARS-CoV-2 articles published by eight major Italian national newspapers between January 1, 2020 and June 15, 2020. These articles underwent a quantitative analysis based on a topic modeling technique. The topic modeling outputs were further analyzed by innovatively combining ad-hoc metrics and a classifier based on the stacking ensemble method (combining regularized logistic regression and linear stochastic gradient descent) for quantifying scientific salience. This enabled the identification of relevant topics and the analysis of the roles that different actors and institutions engaged in making sense of the pandemic. The results show how the health emergency has been addressed primarily in terms of political regulation and concerns and only marginally as a scientific matter. Hence, science has been overwhelmed by politics, which, in media narratives, exerts a moral as well as regulatory authority. Media narratives exclude neither scientific issues nor scientific experts; rather, they configure them as a subsidiary body of knowledge and expertise to be mobilized as an ancillary, impersonal institution useful for legitimizing the expansion of political jurisdiction over the governance of the emergency.

Measuring the overlap between two populations is, in principle, straightforward. Upon fully sampling both populations, the number of shared objects—species, taxonomical units, or gene variants, depending on the context—can be directly counted. In practice, however, only a fraction of each population’s objects are likely to be sampled due to stochastic data collection or sequencing techniques. Although methods exists for quantifying population overlap under subsampled conditions, their bias is well documented and the uncertainty of their estimates cannot be quantified. Here we derive and validate a method to rigorously estimate the population overlap from incomplete samples when the total number of objects, species, or genes in each population is known, a special case of the more general β-diversity problem that is particularly relevant in the ecology and genomic epidemiology of malaria. By solving a Bayesian inference problem, this method takes into account the rates of subsampling and produces unbiased and Bayes-optimal estimates of overlap. In addition, it provides a natural framework for computing the uncertainty of its estimates, and can be used prospectively in study planning by quantifying the tradeoff between sampling effort and uncertainty., Author summary Understanding when two populations are composed of similar species is important for ecologists, epidemiologists, and population geneticists, and in principle it is easy: just sample the two populations, compare the sets of species identified in each, and count how many appear in both populations. In practice, however, this is difficult because sampling methods typically produce only a random subset of the total population, leaving current population overlap estimates biased. Knowing only the number of shared members between two of these partial population samples, this paper shows how we can nevertheless estimate the true overlap between the full populations, when those full populations’ sizes are known. Using Bayesian statistics, we can also compute credible intervals to produce error bars. We show that using this unbiased approach has a dramatic impact on the conclusions one might draw from previously published studies in the malaria literature, which used simple but biased methods. Because the method in this paper quantifies the tradeoff between sampling effort and uncertainty, we also show how to compute the number of samples required to ensure high-confidence results, which may be useful for planning future studies or budgeting lab reagents and time.

BackgroundThis study examines the relationship between experiencing intimate partner violence (IPV), exposure to prior childhood adversity, lifetime adverse experiences, drug-related relationship dependencies with intimate partners and overdose, hospitalization for drug use, friends and family members who overdosed and witnessing overdose.MethodologyThis paper included a sample of 201 women who use drugs in heterosexual relationships with criminal justice-involved men in New York City. We included measures of experiencing overdose, hospitalization for drug use, witnessing overdose, and having friends and family who overdosed. Intimate partner violence consisted of either 1) none/verbal only, 2) moderate and 3) severe abuse. Dichotomous indicators of drug-related relationship dependencies included financial support, drug procurement, splitting and pooling drugs. A scale measured cumulative exposure to childhood adversity and lifetime exposures to adverse events. This paper hypothesized that experiencing moderate and severe IPV, drug-related dependencies and exposure to prior childhood and lifetime adversity would be associated with a greater risk of experiencing overdose, hospitalization for drug use, witnessing overdose and having friends and family members who overdosed. Generalized linear modeling with robust variance estimated relative risk ratios that accounted for potential bias in confidence intervals and adjusted for race, ethnicity, education and marital status.ResultsWe found experiencing moderate or severe IPV was associated with ever being hospitalized for drug use and having a family member who experienced overdose. Experiencing moderate IPV was associated with increased risk of witnessing overdose, Partner drug dependencies were associated with overdose, ever being hospitalized for drug use, witnessing overdose, and having a family member or friend who experienced overdose. Childhood and lifetime adversity exposures were significantly associated with increased risk of overdose, ever being hospitalized for drug use, ever witnessing overdose and having a friend and family member who overdosed.ConclusionFindings underscore the intersection of experiencing IPV and drug-related relationship dependencies, childhood adversity and lifetime adversity in shaping experiences of and witnessing overdose among women who use drugs. They highlight the urgent need to address IPV, adversity experiences and drug-related relationship dependencies in overdose prevention for women who use drugs.

Personal motivation is a key factor in the service of foster care, impending both on the welfare of the child and on the satisfaction of the carer. This paper explores the benefits, challenges and dilemmas involved in the job of professional (i.e. state-supported) foster carer in Romania-a country where the issue of child protection has drawn a great deal of international attention over the last thirty years. The principal hypothesis concerns whether the benefits, challenges and dilemmas identified by foster carers are influenced by the factors that led to their taking up this kind of work. Quantitative research was conducted using a questionnaire as the main tool. The paper takes a descriptive, cross-sectional and multifactorial approach. Sampling was carried out by self-selecting method, and the study involved 51 participants. The research project identified a statistically significant variation in the challenges and dilemmas reported by foster carers. Thus, the results of the study show that the majority of the carers indicate a primarily intrinsic motivation for their work. By way of conclusion, it is argued that social workers, operating in collaboration with multidisciplinary teams, can offer carers support in managing more difficult periods in the child-carer relationship, thus enhancing the sustainability of the foster care service.

During the Covid-19 pandemic, the Vietnamese government has actively implemented various policies to achieve dual objectives: (i) to minimize the loss of life due to the infection; and (ii) to support economic growth. This paper is conducted to examine the effect of the government’s containment and closure policy on the stock market quality in Vietnam. Unlike other papers, we focus exclusively on market quality during the pandemic. We find that the policies appear to positively affect the market quality, except for closing-school policy (negative effect) and international travel (no effect). We argue that the government should sustain the policies until the wide availability of the vaccine to support the stock market quality in the near future.

The number of COVID-19 infections is key for accurately monitoring the pandemics. However, due to differential testing policies, asymptomatic individuals and limited large-scale testing availability, it is challenging to detect all cases. Seroprevalence studies aim to address this gap by retrospectively assessing the number of infections, but they can be expensive and time-intensive, limiting their use to specific population subgroups. In this paper, we propose a complementary approach that combines estimated (1) infection fatality rates (IFR) using a Bayesian melding SEIR model with (2) reported case-fatality rates (CFR) in order to indirectly estimate the fraction of people ever infected (from the total population) and detected (from the ever infected). We apply the technique to the U.S. due to their remarkable regional diversity and because they count with almost a quarter of all global confirmed cases and deaths. We obtain that the IFR varies from 1.25% (0.39–2.16%, 90% CI) in Florida, the most aged population, to 0.69% in Utah (0.21–1.30%, 90% CI), the youngest population. By September 8, 2020, we estimate that at least five states have already a fraction of people ever infected between 10% and 20% (New Jersey, New York, Massachussets, Connecticut, and District of Columbia). The state with the highest estimated fraction of people ever infected is New Jersey with 17.3% (10.0, 55.8, 90% CI). Moreover, our results indicate that with a probability of 90 percent the fraction of detected people among the ever infected since the beginning of the epidemic has been less than 50% in 15 out of the 20 states analyzed in this paper. Our approach can be a valuable tool that complements seroprevalence studies and indicates how efficient have testing policies been since the beginning of the outbreak.

Introduction A number of neglected tropical diseases are targeted for elimination or eradication. An effective surveillance system is critical to determine if these goals have been achieved and maintained. Trachoma has two related but morphologically different presentations that are monitored for elimination, the active infectious form of trachoma and trachomatous trichiasis (TT), the progression of the disease. There are a number of lessons learnt from the Guinea worm surveillance system that are particularly compatible for TT surveillance and the onchocerciasis surveillance system which can provide insights for surveillance of the infectious form of trachoma. Methods/Principal findings A literature search of peer-reviewed published papers and grey literature was conducted using PUBMED and Google Scholar for articles relating to dracunculiasis or Guinea worm, onchocerciasis and trachoma, along with surveillance or elimination or eradication. The abstracts of relevant papers were read and inclusion was determined based on specified inclusion and exclusion criteria. The credibility and bias of relevant papers were also critically assessed using published criteria. A total of 41 papers were identified that were eligible for inclusion into the review. The Guinea worm programme is designed around a surveillance-containment strategy and combines both active and passive surveillance approaches, with a focus on village-based surveillance and reporting. Although rumour reporting and a monetary incentive for the identification of confirmed Guinea worm cases have been reported as successful for identifying previously unknown transmission there is little unbiased evidence to support this conclusion. More rigorous evidence through a randomised controlled trial, influenced by motivational factors identified through formative research, would be necessary in order to consider applicability for TT case finding in an elimination setting. The onchocerciasis surveillance strategy focuses on active surveillance through sentinel surveillance of villages and breeding sites. It relies on an entomological component, monitoring infectivity rates of black flies and an epidemiological component, tracking exposure to infection in humans. Challenges have included the introduction of relatively complex diagnostics that are not readily available in onchocerciasis endemic countries and target thresholds, which are practically unattainable with current diagnostic tests. Although there is utility in monitoring for infection and serological markers in trachoma surveillance, it is important that adequate considerations are made to ensure evidence-based and achievable guidelines for their utility are put in place. Conclusions/Significance The experiences of both the Guinea worm and onchocerciasis surveillance strategies have very useful lessons for trachoma surveillance, pre- and post-validation. The use of a monetary reward for identification of TT cases and further exploration into the use of infection and serological indicators particularly in a post-validation setting to assist in identifying recrudescence would be of particular relevance. The next step would be a real-world evaluation of their relative applicability for trachoma surveillance., Author summary The design of a surveillance system needs to be carefully thought out to ensure it provides sufficient evidence to determine if a disease or infection is eliminated or eradicated. If inappropriate it can lead to on-going transmission and resurgence of infection or disease or the unnecessary continuation of interventions, wasting valuable resources. Guinea worm is a disease that is painful and debilitating, for which there is no drug or vaccine. The aim is to eradicate the disease and as such the Guinea worm programme is designed around a strategy of identification of cases and their containment to prevent onward transmission. Onchocerciasis if left untreated can lead to blindness. The aim is to eliminate the disease through the interruption of transmission. A literature review was conducted to determine available evidence and identify lessons that can be learnt from the surveillance of both diseases for the design of trachoma surveillance strategies in the endgame. The potential utility of rumour reporting and a monetary incentive for the identification of a confirmed case of Guinea worm could be explored for trichiasis case finding. Trichiasis is the progression of trachoma and leads to significant ocular morbidity. The introduction of tests for infection and antibodies and the utility of sentinel surveillance as utilised for onchocerciasis are interesting considerations for active trachoma surveillance post-validation and has potential to identify recrudescence cost-effectively. The experiences of both the Guinea worm and onchocerciasis surveillance strategies have very useful lessons that can be trialled for trachoma surveillance. However, their real-world applicability and implications for trachoma need to be evaluated before any changes in guidelines are proposed.

Sexual violence against women and girls is a major public health problem globally and in South Africa. Although young men have been identified as an important risk group for prevention interventions, scant attention have been given to this age cohort in low and middle-income countries. There is strong evidence that perpetration starts early and increasing attention is being drawn to Higher Education Institutions (HEIs) as settings for prevention interventions. The main objective of this study was to adapt the One Man Can Intervention for use with male university students in residences and develop materials for implementation. This paper presents the qualitative findings of the adaptation process of the One Man Can Intervention with 15 young male student leaders at a HEI in South Africa. The same participants who started in the study, participated throughout. Only five of the 15 participants were located and participated in the interviews six months post intervention. The results show the emergence of a six-hour session adapted intervention that addresses key drivers of violence against women and girls (VAWG). Critical engagement and dialogue on sexual violence is shown to shift key norms on gender equality, on being a man and reflection on their role in preventing sexual violence. This paper contributes to the field where much learning, refining and improvement of prevention interventions for VAWG are ongoing.

Individuals differ widely in their contribution to the spread of infection within and across populations. Three key epidemiological host traits affect infectious disease spread: susceptibility (propensity to acquire infection), infectivity (propensity to transmit infection to others) and recoverability (propensity to recover quickly). Interventions aiming to reduce disease spread may target improvement in any one of these traits, but the necessary statistical methods for obtaining risk estimates are lacking. In this paper we introduce a novel software tool called SIRE (standing for “Susceptibility, Infectivity and Recoverability Estimation”), which allows for the first time simultaneous estimation of the genetic effect of a single nucleotide polymorphism (SNP), as well as non-genetic influences on these three unobservable host traits. SIRE implements a flexible Bayesian algorithm which accommodates a wide range of disease surveillance data comprising any combination of recorded individual infection and/or recovery times, or disease diagnostic test results. Different genetic and non-genetic regulations and data scenarios (representing realistic recording schemes) were simulated to validate SIRE and to assess their impact on the precision, accuracy and bias of parameter estimates. This analysis revealed that with few exceptions, SIRE provides unbiased, accurate parameter estimates associated with all three host traits. For most scenarios, SNP effects associated with recoverability can be estimated with highest precision, followed by susceptibility. For infectivity, many epidemics with few individuals give substantially more statistical power to identify SNP effects than the reverse. Importantly, precise estimates of SNP and other effects could be obtained even in the case of incomplete, censored and relatively infrequent measurements of individuals’ infection or survival status, albeit requiring more individuals to yield equivalent precision. SIRE represents a new tool for analysing a wide range of experimental and field disease data with the aim of discovering and validating SNPs and other factors controlling infectious disease transmission., Author summary Effective approaches to reduce the spread of infectious disease transmission in populations are urgently needed. Reduction in disease spread is most effectively achieved by reducing, separately or in combination, individual (i) “susceptibility”, i.e. the relative risk to become infected when exposed to infectious individuals or material, (ii) “infectivity”, i.e. the propensity to transmit infection to others when infected, and/or by (iii) improving “recoverability”, i.e. the propensity to recover. However, to date it is impossible to assess how these three key epidemiological traits controlling disease transmission in a population are regulated by specific genes or interventions, as the necessary statistical methods for estimating genetic and non-genetic effects from available disease surveillance data don’t exist. This paper introduces a novel statistical method that can estimate, for the first time, genetic and non-genetic effects for host susceptibility, infectivity and recoverability simultaneously from a wide range of realistic disease surveillance data. The method has been incorporated into a user-friendly, freely available software tool called SIRE. SIRE can be applied to a range of experimental and field data and will help to move disease control significantly forward by simultaneously targeting multiple host traits affecting infectious disease spread.

BackgroundThe 2013–15 Ebola epidemic in West Africa was the largest so far recorded, and mainly affected three adjacent countries, Guinea, Liberia and Sierra Leone. The worst affected country (in terms of confirmed cases) was Sierra Leone. The present paper looks at the epidemic in Sierra Leone. The epidemic in this country was a concatenation of local outbreaks. These local outbreaks are not well characterized through analysis using standard numerical techniques. In part, this reflects difficulties in record collection at the height of the epidemic. This paper offers a different approach, based on application of field-based techniques of social investigation that provide a richer understanding of the epidemic.MethodsIn a post-epidemic study (2016–18) of two districts (Bo and Moyamba) we use ethnographic data to reconstruct local infection pathways from evidence provided by affected communities, cross-referenced to records of the epidemic retained by the National Ebola Response Commission, now lodged in the Ebola Museum and Archive at Njala University. Our study documents and discusses local social and contextual factors largely missing from previously published studies.ResultsOur major finding is that the epidemic in Sierra Leone was a series of local outbreaks, some of which were better contained than others. In those that were not well contained, a number of contingent factors helps explain loss of control. Several numerical studies have drawn attention to the importance of local heterogeneities in the Sierra Leone Ebola epidemic. Our qualitative study throws specific light on a number of elements that explain these heterogeneities: the role of externalities, health system deficiencies, cultural considerations and local coping capacities.ConclusionsSocial issues and local contingencies explain the spread of Ebola in Sierra Leone and are key to understanding heterogeneities in epidemiological data. Integrating ethnographic research into epidemic-response is critical to properly understand the patterns of spread and the opportunities to intervene. This conclusion has significant implications for future interdisciplinary research and interpretation of standard numerical data, and consequently for control of epidemic outbreaks.

Dengue is an arbovirus affecting global populations. Frequent outbreaks occur, especially in equatorial cities such as Singapore, where year-round tropical climate, large daily influx of travelers and population density provide the ideal conditions for dengue to transmit. Little work has, however, quantified the peaks of dengue outbreaks, when health systems are likely to be most stretched. Nor have methods been developed to infer differences in exogenous factors which lead to the rise and fall of dengue case counts across extreme and non-extreme periods. In this paper, we developed time varying extreme mixture (tvEM) methods to account for the temporal dependence of dengue case counts across extreme and non-extreme periods. This approach permits inference of differences in climatic forcing across non-extreme and extreme periods of dengue case counts, quantification of their temporal dependence as well as estimation of thresholds with associated uncertainties to determine dengue case count extremities. Using tvEM, we found no evidence that weather affects dengue case counts in the near term for non-extreme periods, but that it has non-linear and mixed signals in influencing dengue through tvEM parameters in the extreme periods. Using the most appropriate tvEM specification, we found that a threshold at the 70th (95% credible interval 41.1, 83.8) quantile is optimal, with extreme events of 526.6, 1052.2 and 1183.6 weekly case counts expected at return periods of 5, 50 and 75 years. Weather parameters at a 1% scaled increase was found to decrease the long-run expected case counts, but larger increases would lead to a drastic expected rise from the baseline correspondingly. The tvEM approach can provide valuable inference on the extremes of time series, which in the case of infectious disease notifications, allows public health officials to understand the likely scale of outbreaks in the long run., Author summary Dengue is an arbovirus affecting populations across much of the globe. Frequent outbreaks occur, especially in equatorial cities such as Singapore, where the year-round tropical climate, large daily influx of travelers and population density provide the ideal conditions for dengue transmission. Little work has however quantified the peaks of dengue outbreaks, when health systems are likely to be most stretched. Nor have methods been developed to infer differences in exogenous factors which lead to the rise and fall of dengue case counts across extreme and non extreme periods. In this paper, we developed time varying extreme mixture (tvEM) methods to account for the temporal dependence of dengue case counts across extreme and non-extreme periods. tvEM is able to infer differences in climatic forcing across non-extreme and extreme periods of dengue case counts, their temporal dependence as well as estimate suitable thresholds with associated uncertainties to determine dengue case count extremities. Using tvEM, we found no evidence that weather affects dengue case counts in the near term for non extreme periods, but has non-linear and mixed signals in influencing dengue through tvEM parameters in the extreme periods. Using the most appropriate tvEM specification, we found that a high percentile threshold is estimated, with dengue outbreak events far larger than currently observed to be expected in 5, 50 and 75 years. Weather parameters at a 1% scaled increase was found to decrease the long-run expected case counts, but larger increases would lead to a drastic expected rise from the baseline correspondingly. tvEM can provide valuable inference on the extremes of time series, which in the case of infectious disease data, allows public health officials to understand factors and the likely scale of infectious disease outbreaks in the long run.

Through national efforts and regional cooperation under the umbrella of the Regional Program for the Elimination of Rabies, dog and human rabies have decreased significantly in Latin America and Caribbean (LAC) countries over the last three decades. To achieve this decline, LAC countries had to develop national plans, and consolidate capabilities such as regular mass dog vaccination, opportune post-exposure prophylaxis and sensitive surveillance. This paper presents longitudinal data for 21 LAC countries on dog vaccination, PEP and rabies surveillance collected from the biannual regional meeting for rabies directors from 1998–2014 and from the Regional Epidemiologic Surveillance System for Rabies (SIRVERA). Differences in human and dog rabies incidence rates and dog vaccination rates were shown between low, middle and high-income countries. At the peak, over 50 million dogs were vaccinated annually in national campaigns in the countries represented. The reported number of animal exposures remained fairly stable during the study period with an incidence rate ranging from 123 to 191 reported exposures per 100,000 people. On average, over 2 million doses of human vaccine were applied annually. In the most recent survey, only 37% of countries reported that they had sufficient financial resources to meet the program objectives. The data show a sufficient and sustained effort of the LAC countries in the area of dog vaccination and provide understanding of the baseline effort required to reduce dog-mediated rabies incidence., Author summary Globally, dog-mediated rabies kills over 55,000 people on an annual basis and disproportionally affects socio-economically disadvantaged populations. For over 4 decades, the Latin America and Caribbean (LAC) region have worked towards the elimination of dog-mediated rabies succeeding in a substantial reduction of both human and dog rabies cases. This paper summarizes the last 15 years of the national and regional rabies elimination efforts, through a series of performance indicators, including government mass dog vaccination campaigns and post-exposure prophylaxis for humans. The most important message from the tail of the rabies epidemic in the LAC Region is one of endurance by the countries and the region to achieve rabies control and elimination. The performance indicators shown in this paper are tools for the LAC region to evaluate its progress and refine its approach, and for other regions to understand the scope of the effort required to reach elimination.

Objective This paper reflected on the prevalence of hypertension and diabetes in Bangladesh, which is spreading rapidly in low-income countries. The rationale of constructing more health centers for addressing NCDs was assessed in this paper by determining the relationship between prevalence of NCDs, particularly hypertension and diabetes, and distance to health facilities. Methods From BDHS (Bangladesh Health and Demographic Survey) 2011 data set, 7544 samples were analyzed to demonstrate association between Non-communicable diseases (NCD) and distance from respondents’ home to health facilities like hospitals, community clinics, pharmacies or doctors’ chambers, and community facilities like market, post office or cinema hall. Bivariate analysis was conducted between accessibility to health facilities and prevalence of the diseases. The causal relationship between the spatial effects and the prevalence of the diseases were analyzed by applying Generalized Linear Mixed Model (GLMM) was fitted. Results Fitting linear mixed effect models, we found that hypertension and diabetes react differently with various spatial effects. Distance from home to hospital had significant effect (P < 0.001) on hypertension showing people living further from the facilities or town centers seemed to be less hypertensive, whereas diabetes showed no such affiliation. Conclusion Higher prevalence of diabetes (40.9%) over hypertension (26.5%) in people aging 35 or higher, have appeared to have caused the difference, which concluded that each non-communicable disease should be dealt to its own merit for policy making instead considering as a group of diseases.

ObjectivesLittle is known about the gender and age differences associated with the effects of economic fluctuations on hospitalization for cardiovascular diseases. This paper investigates the impact of economic fluctuations on hospitalization for ischemic heart disease (IHD), stroke, and hypertension by age and gender between January 1996 and December 2012 in Taiwan.MethodsWe adopted a multiple-input time series analysis to examine the strength of the immediate and latent effects of the 17-year quarterly unemployment rates (UR), air pollution exposure (APE), gross domestic product (GDP), per capita consumption expenditure in cigarette and alcohol (ECA), and per capita healthcare expenditure (HE) on the adjusted quarterly incidence rate of hospitalization. The data used in this paper were retrieved from the National Health Insurance Research Database and the website of the Directorate-General of Budget, Accounting and Statistics (DGBAS), Executive Yuan.ResultsOur findings indicate that higher UR increased IHD hospitalization in young men and women and middle-aged women but reduced stroke hospitalization in young men. Higher APE increased IHD hospitalization in young men but reduced it for young women, increased stroke hospitalization in old men and middle-aged women but reduced it for young men, and increased hypertension hospitalization in middle-aged men and young women. Higher ECA reduced IHD hospitalization in middle-aged men, increased stoke hospitalization in middle-aged and old men and middle-aged women. Higher HE reduced IHD hospitalization in old men, young and old women, reduced stroke hospitalization in old women, and reduced hypertension hospitalization in young and middle-aged women.ConclusionsOverall, we found that the economic fluctuations caused increased harmful effects in certain population subgroups but also brought some soothing effects to some groups.

Introduction Stigma and discrimination (SAD) related to HIV compromise access and adherence to treatment and support programs among people living with HIV (PLHIV). The ambitious goal of ending the epidemic of HIV by 2030 set by the United Nations Joint Program of HIV/AIDS (UNAIDS) will thus only be achieved if HIV-related stigma and discrimination are reduced. The objective of this review was to locate, appraise and describe international literature reporting on interventions that addressed HIV-related SAD in healthcare settings. Methods The databases searched were: Cumulative Index to Nursing and Allied Health (CINAHL), Excerpta Medica Database from Elsevier (EMBASE), PubMed and Psychological Information (PsycINFO) database. Two individuals independently appraised the quality of the papers using appraisal instruments from the Joanna Briggs Institute (JBI). Data were extracted from papers included in the review using the standardized data extraction tool from JBI. Quality of evidence for major outcomes was assessed using Grading of Recommendations, Assessment, Development and Evaluation (GRADE). Results We retained 14 records reporting on eight studies. Five categories of SAD reduction (information-based, skills building, structural, contact-based and biomedical interventions) were identified. Training popular opinion leaders (POLs) resulted in significantly lower mean avoidance intent scores (MD = -1.87 [95% CI -2.05 to -1.69]), mean prejudicial attitude scores (MD = -3.77 [95% CI -5.4 to -2.09]) and significantly higher scores in mean compliance to universal precaution (MD = 1.65 [95% CI 1.41 to 1.89]) when compared to usual care (moderate quality evidence). The Summary of Findings table (SOF) is shown in Table 1. Conclusions Evidence of moderate quality indicates that training popular opinion leaders is effective in reducing avoidance intent and prejudicial attitude and improving compliance to universal precaution. Very low quality evidence indicates that professionally-assisted peer group interventions, modular interactive training, participatory self-guided assessment and intervention, contact strategy combined with information giving and empowerment are effective in reducing HIV-related stigma.Further Randomized Controlled Trials (RCTs) are needed. Future trials need to use up-to-date and validated instruments to measure stigma and discrimination.